Browse > Article
http://dx.doi.org/10.7471/ikeee.2018.22.4.1140

Wavelet-based Statistical Noise Detection and Emotion Classification Method for Improving Multimodal Emotion Recognition  

Yoon, Jun-Han (Dept. of Computer Engineering, Seokyeong University)
Kim, Jin-Heon (Dept. of Computer Engineering, Seokyeong University)
Publication Information
Journal of IKEEE / v.22, no.4, 2018 , pp. 1140-1146 More about this Journal
Abstract
Recently, a methodology for analyzing complex bio-signals using a deep learning model has emerged among studies that recognize human emotions. At this time, the accuracy of emotion classification may be changed depending on the evaluation method and reliability depending on the kind of data to be learned. In the case of biological signals, the reliability of data is determined according to the noise ratio, so that the noise detection method is as important as that. Also, according to the methodology for defining emotions, appropriate emotional evaluation methods will be needed. In this paper, we propose a wavelet -based noise threshold setting algorithm for verifying the reliability of data for multimodal bio-signal data labeled Valence and Arousal and a method for improving the emotion recognition rate by weighting the evaluation data. After extracting the wavelet component of the signal using the wavelet transform, the distortion and kurtosis of the component are obtained, the noise is detected at the threshold calculated by the hampel identifier, and the training data is selected considering the noise ratio of the original signal. In addition, weighting is applied to the overall evaluation of the emotion recognition rate using the euclidean distance from the median value of the Valence-Arousal plane when classifying emotional data. To verify the proposed algorithm, we use ASCERTAIN data set to observe the degree of emotion recognition rate improvement.
Keywords
Kurtosis; Skewness; Wavelet transform; Hampel identifier; Euclidan distance;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Mrs V Baby Deepa, Dr P Thangaraj, Dr S Chitra, "Investigating the performance improvement by sampling techniques in EEG data," International Journal on Computer Science and Engineering, Vol.2, pp.2025-2028, 2010.
2 J. Hernandez et al. "Call center stress recognition with person-specific models," Proceedings of the Affective Computing and Intelligent Interaction, Memphis, 2011. DOI:10.1007/978-3-642-24600-5_16   DOI
3 M. Poh et al. "A Wearable Sensor for Unobtrusive, Long-Term Assessment of Electrodermal Activity," IEEE Trans. Biomed. Eng, 2010. DOI:10.1109/TBME.2009.2038487   DOI
4 M. Poh et al. "Continuous monitoring of electrodermal activity during epileptic seizures using a wearable sensor," Annual Int. Conf. of the IEEE Engineering in Medicine and Biology Society (EMBC), 2010. DOI:10.1109/IEMBS.2010.5625988   DOI
5 R. R. Coifman and D. L. "Donoho. in Wavelets and statistics," Vol.103, pp.281-299, 1995.
6 D. P. Subha, P. K. Joseph, R. Acharya, C. M. Lim, "EEG Signal Analysis: A Survey," Journal of Medical Systems, Vol.34, No.2, pp 195-212, 2010. DOI:10.1007/s10916-008-9231-z   DOI
7 Chmelka and J. Kozumplik, "Wavelet-based Wiener Filter for electrocardiogram signal denoising," Comput. Cardiol, vol.32, pp.771-774, 2005. DOI:10.1109/CIC.2005.1588218   DOI
8 C. Li, C. Xu, Z. Feng, "Analysis of physiological for emotion recognition with the IRS model," Neurocomputing Vol.178 pp.103-111, 2016. DOI:10.1016/j.neucom.2015.07.112   DOI
9 M. Soleymani, J. Lichtenauer, T. Pun, M. Pantic, "A multi-modal database for affect recognition and implicit tagging," IEEE Trans. Affect. Comput. Vol.3 pp.42-55, 2012. DOI:10.1109/T-AFFC.2011.25   DOI
10 G. Fanelli, J. Gall, H. Romsdorfer, T. Weise, L. Van Gool, "A 3-D audio-visual cor-pus of affective communication," IEEE Trans. on Multimedia Vol. 12 pp.591-598. 2010. DOI:10.1109/TMM.2010.2052239   DOI
11 Z. Yin, J. Zhang, "Operator functional state classification using least-square sup-port vector machine based recursive feature elimination technique," Comput. Methods Prog. Biomed. Vol. 113, pp.101-115. 2014. DOI:10.1016/j.cmpb.2013.09.007   DOI
12 R. Memisevic and C. Zach, "Gated softmax classification," Advances in Neural, pp.1-9, 2010.
13 Maarten Jansen, "wavelet Thresholding and Noise Reductionm," Katholieke Universiteit Leuven-Faculteit Toegepaste Wetenschappen Arenbergkasteel, 2000.
14 R. Subramanian et al., "ASCERTAIN: Emotion and Personality Recognition using Commercial Sensors," IEEE Transactions on Affective Computing, pp.1-14, 2016.
15 P. Baldi, "Boolean AutoEncoder," pp.37-50, 2012.
16 R. J. Davidson, "Affective neuroscience and psychophysiology: toward a synthesis," Psychophysiology Vol.40, pp.655-665, 2003. DOI:10.1111/1469-8986.00067   DOI
17 J. Kim, E. Andre, "Emotion-specific dichotomous classification and feature-level fusion of multichannel biosignals for automatic emotion recognition," in: Pro-ceedings of IEEE Inter-national Conference on Multisensor Fusion and Integration for Intelligent Systems 2008. DOI:10.1109/MFI.2008.4648119   DOI
18 H. Lee, A. Shackman, D. Jackson, R. Davidson, "Test-retest reliability of voluntary emotion regulation," Psychophysiology Vol.46 pp.874-879. 2009. DOI:10.1111/j.1469-8986.2009.00830.x   DOI
19 O. AlZoubi, S. K. D'Mello, R. A. Calvo, "Detecting naturalistic expressions of non-basic affect using physiological signals," IEEE Trans. Affect. Comput. Vol.3 pp.298-310. 2012. DOI:10.1109/T-AFFC.2012.4   DOI
20 L. Brown, B. Grundlehner, J. Penders, "Towards wireless emotional valence de-tection from EEG," in: Proceedings of Engineering in Medicine and Biology Society, EMBC, 2011. DOI:10.1109/IEMBS.2011.6090412   DOI
21 Z. Yin, J. Zhang, "Identification of temporal variations in mental workload using locally-linear-embedding-based EEG feature reduction and support-vec tor-machine-based clustering and classification technique," Comput. Methods Prog. Biomed. Vol.115, pp.119-134. 2014. DOI:10.1016/j.cmpb.2014.04.011   DOI
22 G. K. Verma, U. S. Tiwary, "Multimodal fusion framework: a multiresolution ap-proach for emotion classification and recognition from physiological signal," NeuroImage Vol.102 pp.162-172. 2014. DOI:10.1016/j.neuroimage.2013.11.007   DOI
23 M. Khezri, M. Firoozabadi, A. R. Sharafat, "Reliable emotion recognition system based on dynamic adaptive fusion of forehead biopotentials and physiological signals," Comput. Methods Prog. Biomed. Vol.122 pp.149-164. 2015. DOI:10.1016/j.cmpb.2015.07.006   DOI
24 Senthil, R. Arumuganathan, K. Sivakumar, and C. Vimal, "Removal of ocular artifacts in the EEG through wavelet transform without using an EOG reference channel," Int.J. Open Problems Compt. Math., Vol.1, No.3, 2008. DOI:10.1.1.502.6932